125,916 research outputs found
A distributed forward-backward algorithm for stochastic generalized Nash equilibrium seeking
No full textWe consider the stochastic generalized Nash equilibrium problem (SGNEP) with expected-value cost functions. Inspired by Yi and Pavel (2019), we propose a distributed generalized Nash equilibrium seeking algorithm based on the preconditioned forward-backward operator splitting for SGNEPs, where, at each iteration, the expected value of the pseudogradient is approximated via a number of random samples. Our main contribution is to show almost sure convergence of the proposed algorithm if the pseudogradient mapping is restricted (monotone and) cocoercive.Accepted Author ManuscriptTeam Bart De Schutte
Stochastic Generalized Nash Equilibrium-Seeking in Merely Monotone Games
Full text linkWe solve the stochastic generalized Nash equilibrium (SGNE) problem in merely monotone games with expected value cost functions. Specifically, we present the first distributed SGNE-seeking algorithm for monotone games that require one proximal computation (e.g., one projection step) and one pseudogradient evaluation per iteration. Our main contribution is to extend the relaxed forward–backward operator splitting by the Malitsky (Mathematical Programming, 2019) to the stochastic case and in turn to show almost sure convergence to an SGNE when the expected value of the pseudogradient is approximated by the average over a number of random samples.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Team Sergio GrammaticoTeam Bart De Schutte
Training Generative Adversarial Networks via Stochastic Nash Games
No full textGenerative adversarial networks (GANs) are a class of generative models with two antagonistic neural networks: a generator and a discriminator. These two neural networks compete against each other through an adversarial process that can be modeled as a stochastic Nash equilibrium problem. Since the associated training process is challenging, it is fundamental to design reliable algorithms to compute an equilibrium. In this article, we propose a stochastic relaxed forward-backward (SRFB) algorithm for GANs, and we show convergence to an exact solution when an increasing number of data is available. We also show convergence of an averaged variant of the SRFB algorithm to a neighborhood of the solution when only a few samples are available. In both cases, convergence is guaranteed when the pseudogradient mapping of the game is monotone. This assumption is among the weakest known in the literature. Moreover, we apply our algorithm to the image generation problem.</p
Hepatitis B virus–induced hepatocarcinogenesis: A virological and oncological perspective
No full textHepatitis B virus (HBV) is a partially double-stranded DNA virus associated with hepatocellular carcinoma (HCC). The viral integration into the hepatocyte genome, the viral protein-induced oncogenesis, the increased hepatocyte turnover and the chronic inflammatory response towards HBV are all hypothesized mechanisms for the development of HCC. The fact that HBV infection and HCC prevalence show different correlations in various regions of the world indicates that there may be virus-independent phenomena for cancer development in these regions. From this point of view, it is important to review our knowledge and to examine the relationship between HBV and HCC in the light of current data. In this article, we investigate the relationship between HBV and HCC by presenting epidemiological and microbiological data, accompanied by the principles of viral oncogenesis
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Stochastic generalized Nash equilibrium seeking under partial-decision information
Full text linkWe consider for the first time a stochastic generalized Nash equilibrium problem, i.e., with expected-value cost functions and joint feasibility constraints, under partial-decision information, meaning that the agents communicate only with some trusted neighbors. We propose several distributed algorithms for network games and aggregative games that we show being special instances of a preconditioned forward–backward splitting method. We prove that the algorithms converge to a generalized Nash equilibrium when the forward operator is restricted cocoercive by using the stochastic approximation scheme with variance reduction to estimate the expected value of the pseudogradient.Team Sergio GrammaticoTeam Bart De Schutte
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Corrigendum to “Stochastic generalized Nash equilibrium seeking under partial-decision information” [Automatica 137 (2022) 110101] (Automatica (2022) 137, (S0005109821006300), (10.1016/j.automatica.2021.110101))
No full textThe following paragraph was inadvertently omitted from the final version of the paper (Franci & Grammatico, 2022). The paragraph was to be placed at the end of the Introduction section: “Finally, let us remark that our setting, and in general, the literature on SGNEPs, assumes that the agents have access to stochastic (partial) first-order information, specifically, random samples of the pseudogradient, as opposed to zeroth-order information, i.e., direct measurements of the cost functions as in extremum seeking (Frihauf, Krstic, & Basar, 2011; Krilašević & Grammatico, 2021; Liu & Krstić, 2011). In particular, the SGNEP literature assumes that the random samples of first-order information are given, hence cannot be controlled, while the extremum-seeking literature assumes that the available zeroth-order information is deterministic and results from a controlled perturbation injected into the system”.Corrigendum DOI 10.1016/j.automatica.2021.110101Team Bart De SchutterTeam Sergio Grammatic
Convergence of sequences: A survey
No full textConvergent sequences of real numbers play a fundamental role in many different problems in system theory, e.g., in Lyapunov stability analysis, as well as in optimization theory and computational game theory. In this survey, we provide an overview of the literature on convergence theorems and their connection with Féjer monotonicity in the deterministic and stochastic settings, and we show how to exploit these results.Team Sergio GrammaticoTeam Bart De Schutte
Finite-time influence systems and the wisdom of crowd effect
No full textRecent contributions have studied how an influence system may affect the wisdom of crowd phenomenon. In the so-called naïve learning setting, a crowd of individuals holds opinions that are statistically independent estimates of an unknown parameter; the crowd is wise when the average opinion converges to the true parameter in the limit of infinitely many individuals. Unfortunately, even starting from wise initial opinions, a crowd subject to certain influence systems may lose its wisdom. It is of great interest to characterize when an influence system preserves the crowd wisdom effect. In this paper we introduce and characterize numerous wisdom preservation properties of the basic French-DeGroot influence system model. Instead of requiring complete convergence to consensus as in the previous naïve learning model by Golub and Jackson, we study finite-time executions of the French-DeGroot influence process and establish in this novel context the notion of prominent families (as a group of individuals with outsize influence). Surprisingly, finite-time wisdom preservation of the influence system is strictly distinct from its infinite-time version. We provide a comprehensive treatment of various finite-time wisdom preservation notions, counterexamples to meaningful conjectures, and a complete characterization of equal-neighbor influence systems
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